Rank Listing of Competitive Performances of Exercise on a Machine

ABSTRACT

Among other things, a processor executes instructions to: update, in real time during a current instance of an exercise activity by a subject competitor, a graphical user interface displayed on a display device, the display device being included in a first exercise machine operated by the subject competitor or included in a mobile electronic device. The graphical user interface includes: a ranking of the subject competitor and a second competitor based on (i) a projected performance metric of the subject competitor over a predefined scope of the exercise activity compared to (ii) a historical performance metric of the second competitor over the predefined scope of the exercise activity in a previous instance of the exercise activity, an illustration of a margin between the historical performance metric of the second competitor and the projected performance metric for the subject competitor, and an illustration of the projected performance metric.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application and claims priority under35 U.S.C. § 120 to U.S. patent application Ser. No. 17/001,285, filedAug. 24, 2020 (issued as U.S. Pat. No. 11,229,825 on Jan. 25, 2022),which is incorporated here by reference in its entirety.

BACKGROUND

This description relates to rank listing of competitive performances ofexercise on a machine.

Leaderboards are a form of rank listing often used to present tocompetitors and observers the relative progress of leaders during and upto an end time or other end point of a competition, such as golf ortrack or rowing. In some kinds of competition, such as those in whichthe competitors use instrumented exercise machines (for example, cyclingor rowing machines), progress of the competitors can be measuredcontinuously and the results compared frequently (such as every fewseconds) to show current leaderboard information. Relative performanceof competitors can be reported in terms of variables such as distancecovered since the start of the competition.

Exercise machines can be used in a “live” mode for live real-timecompetitive exercise activities, in an “on-demand” mode for virtualcompetitive exercise activities, or in a combination of the two.

In some uses of exercise machines in a live mode, live competitorslocated remotely from one another compete in real time. Live real-timevideo and performance data for a live competitor can be presented to theother live competitors through displays at their exercise machines toenhance their competitive experience.

In some typical uses of exercise machines in an on-demand mode, a livesubject competitor engages in a virtual competition with othercompetitors whose prior performances for the exercise activity have beenpreviously stored as historical performance data. The other competitorscan be considered virtual competitors in that they are not competinglive and in real-time but rather their historical performance data isused to create the impression of virtual competition of the virtualcompetitors versus the subject competitor. When such stored historicalperformance data is available, the subject competitor can engage in thevirtual competitive exercise activity by choosing that exercise activitythrough a user interface of the exercise machine at a time and in acontext convenient to the subject competitor. In some cases, one of thevirtual competitors can be the subject competitor herself in an instancewhen she previously engaged in exercise activity. In other words she canbe competing against her prior performance (a prior instance) of thesame exercise activity.

Historical performance data for a competitor engaging in an exerciseactivity can include speed, distance traveled, heart rate, stroke rate,watts, and calories burned, at closely spaced exercise moments duringthe exercise activity.

In either an on-demand mode or a live mode of competition, anelectronically determined leaderboard can be presented to the subjectcompetitor.

SUMMARY

In general, in an aspect, a processor executes instructions to (a)during a current instance of an exercise activity having a predefinedscope and being performed by a subject competitor on a machine, computea first performance metric for the subject competitor using performancedata from the subject competitor's performance of a lesser scope thanthe predefined scope of the current instance of the exercise activity,the first performance metric being normalized to reflect a hypotheticalperformance over the predefined scope, (b) receive performance datarepresenting an historical performance during a previous instance of theexercise activity by at least one other competitor on a machine, and (c)present to the subject competitor comparative data based on the firstperformance metric and on a second performance metric for the historicalperformance by at least one other competitor that is based on thereceived performance data and that reflects performance of the at leastone other competitor of a scope of the previous instance that issubstantially the same as the predefined scope.

Implementations can include one or a combination of two or more of thefollowing features. The exercise activity includes rowing. Thepredefined scope includes a time duration. The predefined scope includesa distance. The machine includes a rowing machine. The performancemetric includes a distance predicted to be covered during the predefinedscope of the exercise activity. The performance data includes distancedata. The performance metric includes a time predicted to have elapsedfor the predefined scope of the exercise activity. The performancemetric includes an average speed as of a current exercise moment. Thesubject competitor's performance of less than the predefined scopeincludes the subject competitor's performance for a time durationshorter than a time duration of the predefined scope. The comparativedata includes a predicted value of the first performance metric for thesubject competitor. The predicted value of the first performance metricincludes a predicted distance. The predicted value of the firstperformance metric for the subject competitor is based on an actualvalue of the first performance metric for the subject competitor'sperformance of the lesser scope. The predicted value of the firstperformance metric for the subject competitor is based on an actualvalue of the second performance metric for the historical performance byat least one other competitor's performance of the lesser scope. Thepredicted value of the first performance metric for the subjectcompetitor is based on the proportion of the predefined scoperepresented by the lesser scope. The predicted value of the firstperformance metric is based on a rank of the subject competitor based onthe subject competitor's performance of the lesser scope. Presenting thecomparative data includes displaying the comparative data in aninteractive user interface accessible to the subject competitor.Presenting the comparative data includes presenting data representing aperformance of the subject competitor and data representing a relativeperformance of at least one other competitor compared to the subjectcompetitor. The at least one other competitor is a live competitor inthe current instance of the exercise activity and the receivedperformance data is of the historical performance of the live competitorduring a previous instance of the exercise activity.

In general, in an aspect, a processor executes instructions to (a)receive from an exercise machine current performance data indicative ofa performance metric for a subject competitor performing a currentinstance of an exercise activity having a predefined scope, the currentperformance data being indicative of a performance metric for a lesserscope than the predefined scope, (b) receive historical performance datarepresenting an historical performance during a previous instance of theexercise activity by at least one other competitor on a machine, and (c)use the current performance data and the historical performance data tocompute the performance metric including normalizing the performancemetric to represent a hypothetical performance of the subject competitorover the predefined scope.

Implementations can include one or a combination of two or more of thefollowing features. The exercise activity includes rowing. Thepredefined scope includes a time duration. The machine includes a rowingmachine. The performance metric includes a distance predicted to becovered during the predefined scope of the exercise activity. Thecurrent performance data and the historical performance data includedistance data. The current performance data is indicative of aperformance metric for the subject competitor's performance of less thanthe predefined scope. The performance metric includes a predictedperformance metric for the predefined scope. The predicted performancemetric for the subject competitor is based on an actual value of theperformance metric for the subject competitor's performance of thelesser scope. The predicted performance metric for the subjectcompetitor is based on an actual value of the historical performance ofthe lesser scope by at least one other competitor. The predictedperformance metric for the subject competitor is based on the proportionof the predefined scope represented by the lesser scope. The performancemetric of the subject competitor is presented. The historicalperformance by at least one other competitor on a machine is presented.

In general, in an aspect, a processor executes instructions to (a)during a current instance of an exercise activity having a predefinedscope and being performed by a subject competitor on a machine, computea performance metric for the subject competitor using performance datafrom the subject competitor's performance of a lesser scope than thepredefined scope of the current instance of the exercise activity, and(b) apply a straight-line projection to the performance metric for thelesser scope projection to predict the performance metric for thepredefined scope of the exercise activity.

Implementations may include one or a combination of two or more of thefollowing features. The exercise activity comprises rowing. Thepredefined scope comprises a time duration. The machine comprises arowing machine. The performance metric comprises a distance predicted tobe covered during the predefined scope of the exercise activity. Theperformance data comprises distance data. The subject competitor'sperformance of less than the predefined scope comprises the subjectcompetitor's performance for a time duration shorter than a timeduration of the predefined scope. The predicted performance metriccomprises a predicted distance. The predicted performance data isdisplayed in an interactive user interface accessible to the subjectcompetitor.

These and other aspects, features, implementations, and advantages (a)can be expressed as methods, apparatus, systems, components, programproducts, business methods, means or steps for performing functions, andin other ways, and (b) will become apparent from the followingdescription and from the claims.

DESCRIPTION

FIG. 1 is a block diagram.

FIG. 2 is a table including a rank listing.

FIG. 3 is a user interface.

As shown in FIG. 1, here we describe a rank listing technology 10 thatcan be used to generate (and present) rank listings 12 of competitorsengaged in an exercise activity. The competitors can include a currentsubject competitor 14 engaged in, for example, an on-demand modeexercise activity on an exercise machine 16, one or more virtualcompetitors 18, and, in some cases, one or more other currentcompetitors 20, concurrently or previously engaged in the same exerciseactivity on exercise machines 22, 24. (Note that in someimplementations, the performances of other current competitors will notbe included in the presented rank listing unless and until they reachthe end of the exercise activity.) Each of the exercise machines can beequipped with electronic instruments 26 to measure and generateperformance data for each of the competitors with respect to one or moreperformance metrics 28 for the exercise activity. We sometimes refer toan occasion on which a subject competitor or a virtual competitorengages in an exercise activity as an “instance”.

In some cases, when the exercise machine is a rowing machine, the linearmotion of the handle is converted to rotary motion through a drivetrain.Drivetrain components are coupled to rotary encoders to produceelectronic signals proportional to changes in angle of a rotating shaft.The signals are monitored at regular time intervals by amicrocontroller, which is therefore able to compute position, velocity,and acceleration of the rotating components. Measured angular motion isthen used to generate exercise performance metrics according to aphysical model of the functioning of the exercise machine. Distance, onespecific performance metric that can be computed for a rowing machine,is a function of the amount of rotation measured from the machine'sflywheel and the amount of braking torque imposed by the rowing machineto decelerate the flywheel. A rower is determined to have covered moredistance if she is producing more watts while rowing.

The measured performance data can be used immediately as an indicator ofthe subject competitor's performance, or can be stored as historicalperformance data for later use in representing a virtual competitorduring a competition.

The rank listings can be generated by a processor 29 executinginstructions 31 stored on a tangible storage 33 using real-timeperformance data 30 for the subject competitor and any of the othercurrent competitors and using stored historical performance data 32 forany of the virtual competitors. (Note that in some implementations, theperformances of other current competitors will not be included in thepresented rank listing unless and until they reach the end of theexercise activity.) The processor and tangible storage can be located ata server 35 which communicates through the Internet or othercommunication network 37 with the electronic instruments at the exercisemachines. In some cases, the electronic instruments can include or becontrolled by a computational device 39 such as a dedicated computer ora portable smart phone or tablet. A display 41 on the computationaldevice can be used to present the rank listing to the subject competitoror another live competitor. The server can send the rank listing orinformation to generate the rank listing through the network to thecomputational device for presentation through a user interface shown onthe display.

The rank listing can include a list of two or more entries 34, each fora corresponding competitor. Each entry on the rank listing can includean identifier 42 of the competitor and indicators 43, 45 of thecompetitors' relative performances at one or more times 53 (“exercisemoments”) during the exercise period 51 of the exercise activity (thatis, the period beginning with the start 47 and ending with the finish 49of the exercise activity). For this purpose, the indicators of relativeperformances can be of predicted performance metrics for the subjectcompetitor and of historical final metrics for the virtual competitors.We sometimes refer to the exercise period as a “predefined scope” of theexercise activity. When an exercise moment occurs before the end of theexercise period (that is, before the end of the “predefined scope”), wesometimes refer to the period from the start of the exercise activity tothe current exercise moment as a “lesser scope”. In some examples, theexercise activity may be considered to have been completed when a finaldistance (say 5000 meters) or a final time period (say 5 minutes) hasbeen reached. Yet the predefined scope could be shorter in distance (say4000 meters) or in time (say 4 minutes) and the lesser scope would beshorter in distance or time than the predefined scope.

A variety of performance metrics can be used for measuring the relativeperformances of two or more competitors for a given type of exerciseactivity and for reporting their relative performance in a rank listingat each of a succession of performance moments. One such performancemetric is a distance covered on a real or hypothetical exercise courseassociated with the exercise activity (for example, a running, cycling,or rowing course) for a given period of time (the “predefined scope”).Various distance metrics could be used, such as an interim distancecovered by a competitor from the start of the exercise period and up toa particular exercise moment (for example, a “lesser scope”), a finaldistance covered by the competitor for the entire exercise period (the“predefined scope”), a predicted distance anticipated to be covered by acompetitor as of a particular future exercise moment, or a predictedfinal distance anticipated to be covered by a competitor for an entireexercise period. In some cases, the performance metric could be theamount of time that elapses for the competitor to cover a predefineddistance. Other parameters for the performance metric and predefinedscope could also be used such as the average speed as of a currentexercise moment In the latter case, the subject competitor's averagespeed as of the current exercise moment can be presented on the ranklisting with the final average speeds of the virtual competitors.

In an on-demand mode, the rank listing can report the performances of asubject competitor and of one or more virtual competitors even thoughthe subject competitor is not then one of the top performers. In otherwords, the rank listing need not be a literal leaderboard in the sensethat the rank listing may not report the performances of thetop-performing competitors. In some examples, however, the virtualcompetitors identified on the rank listing may include the virtualcompetitors who had the best performances or the virtual competitorswhose performances are next above or next below the subject competitorin rank. In some cases, the choice of which virtual competitors topresent can be selected in other ways. In some instances, the subjectcompetitor can specify through a user interface the competitors whoseperformances should be shown on the rank listing with the subjectcompetitor.

Computation and Reporting of Performances and Ranks in an On-Demand Mode

As shown by example in FIG. 2 for an exercise activity that is a10-minute rowing exercise, the rank listing is shown as of the 5-minuteexercise moment, halfway through the exercise activity. The rank listingcould be updated at regular frequent intervals, for example, everysecond, two seconds, ten seconds, or minute. Using two seconds rendersthe rank listing current enough for a typical competitor but not sofrequent as to be jarring.

The rank listing 60 shows the projected rank 66 of the subjectcompetitor (called “you”). In this case, the predicted rank is 144^(th)as of this exercise moment. The rank listing also includes entries 68for five other competitors, in this case virtual competitors. Each ofthe virtual competitors is identified by a letter 70. For each of thevirtual competitors, the rank listing shows the differential distance 81(in this case in meters) by which the virtual competitor is anticipatedto be ahead of or behind the subject competitor as of the end of theexercise period. Column 72 also shows the anticipated distance that thesubject competitor will have rowed at the end of the exercise period(e.g., the end of the “predefined scope”), in this case 2030 meters.

As a result the subject competitor in an on-demand exercise activitywill see, and easily and quickly be able to evaluate, her predicted rankas of the end of the exercise period (predefined scope) , how far shecan expect to have rowed at the end of the exercise period, and how far(in distance) she will then be ahead or behind or even with a selectednumber of identified virtual competitors who have previously completedthe same exercise activity. Column 74, which may or may not be reportedon the rank listing, shows the actual distance rowed by each of thevirtual competitors for the full exercise period, according to thehistorical performance data.

Table 76 of FIG. 2 shows historical performance data for the fivevirtual competitors covered by the rank listing, namely the five virtualcompetitors whose actual historical performance data for distance rowedas of the end of the exercise activity is closest to (above or below)the anticipated distance rowed by the subject competitor. The part ofthe rank listing 60 that shows distance differences for the virtualcompetitors compared to the subject competitor can be created from thedata in table 76.

Table 78 of FIG. 2 shows the steps in calculating the value 80 of therank listing 72, that is, for generating a predicted final distance ofthe subject competitor. In this example, the current elapsed time is 300minutes (line 82). The exercise period is 600 minutes (line 84). Thepart of the workout that is done is 50% (line 86). Other approaches canalso be used for generating predicted final distances.

The number of meters that the subject competitor is currently behind thenext best performing virtual competitor is −10 meters (line 88). Line 88is determined by subtracting, from the distance covered by the subjectcompetitor determined at 5:00 (in this case 1000 meters, line 112), theknown distance covered by the virtual competitor as of 5:00 into theexercise period (in this case 1100 meters, line 110 of table 108).

Line 90 is the anticipated difference as of the end of the exerciseperiod, calculated as the number of meters that the subject competitoris behind the next best performing virtual competitor divided by thepercentage of the exercise period completed (in this example, 50%).

Line 90 is the number of meters that the virtual competitor who is nextahead of the subject competitor covered by the end of the exerciseperiod based on historical performance data (in this case, 2050). Line92 is the subject competitor's number of meters at the end of theexercise period net of the difference shown in line 90 (that is,2030=2050−20).

In this example, the subject competitor is in 143rd place at 5:00 and isprojected to have fallen in rank by one position to 144th as of the endof the exercise activity.

Historical Performance Data

In some cases, the technology maintains historical performance data forevery competitor who has participated in an instance of the particularexercise activity for use (among other things) in reporting informationon the rank listing in future competitions. The number of suchcompetitors for whom historical performance data is stored could be anynumber from 0 to a very large number (hundreds or even thousands ormillions).

If no one has previously participated in the particular exerciseactivity and the subject competitor is the first to do so, the predictedfinal distance of the subject competitor's performance can be calculatedas shown in table 96 for an example in which the calculation is beingmade as of 25% (line 102, that is, 150 seconds, line 98) into the600-seconds exercise period (line 100). The measured distance covered asof that moment is 1000 meters (line 104) and the predicted finaldistance is the current distance divided by the percentage of completion(line 106, 4000=1000/25%). In effect the server uses a straight-lineprojection. Other mathematical operations could be used to generate aprediction based on, for example, workout intensity, stroke rate,historical data, or other information. The subject competitor will bethe only competitor shown on the rank listing.

User Interface Presentation

As shown in FIG. 3, in some implementations, the presentation of therank listing 300 on a user interface 302 includes an entry 304 for thesubject competitor and entries 306 for each of five virtual competitorstwo of whom are ranked immediately lower and three of whom are rankedimmediately higher than the subject competitor. The entry for eachvirtual competitor presents a badge 308 including the first twocharacters of the pseudonym, and a set of information about the virtualcompetitor including a pseudonym 310, gender 312, age bracket 314, andaddress indicator 316. At the right end of the entry is a differentialdistance number 318 representing a difference between the actualhistorical final distance of the virtual competitor and the predictedfinal distance of the subject competitor. In the example shown, threevirtual competitors each had an actual historical final distance 1 meterahead of the predicted final distance of the subject competitor and twovirtual competitors each had an actual historical final distance thesame as the predicted final distance of the subject competitor.

In some cases, the badge can contain an avatar with a photograph of thecompetitor (or any other image) and/or an oar blade representing thatcompetitor's affiliation.

The entry 304 for the subject competitor shows his current rank 320, abadge 322 showing an image 324, and a predicted final distance 325. Thetotal number of virtual competitors 326 is shown at the top of the userinterface presentation.

In addition to presenting to the subject competitor her predicted finaldistance in conjunction with differential distances for the virtualcompetitors, the user interface can provide an option for showing thesubject competitor's actual distance covered as of the exercise momentbeing presented. Note that, in FIG. 3, the actual distance option ispresented using the number 19 m.

The user interface includes a filter button 327 that enables the user tofilter the entries on the rank listing according to gender or decade ofage (e.g., 50s, 30s). Filtering could be done on any other arbitraryattributes such as rower affiliation, geographic location, or interests,among other things. Then the subject competitor's rank according to herpredicted final distance can be determined based on all competitors, butthe rank listing can present only the filtered members.

This should provide bigger samples for better estimates of rank for newexercise activities having few virtual competitors, and for less commonfilters; e.g., non-binary 70+. A straight-line prediction of the subjectcompetitor's final distance might be used instead.

Effects of the Technology

The rank listing described above reflects the subject competitor'scurrent rank in the exercise activity at each successive exercisemoment, and gives the subject competitor the advantage of knowing thefinal distance she will need to achieve to beat the virtual competitorswho have nearby ranks. The presented ranks of the virtual competitorsare static and do not change throughout the exercise activity becausethey reflect fixed historical performance data. Only the rank of thesubject competitor relative to the virtual competitors can change as aresult of, for example, greater or lesser effort exerted by the subjectcompetitor.

Using the subject competitor's current rank as the basis of predictionof the subject competitors final distance should be more stable thanusing a straight line projection technique, for exercise activities thathave varying intensities during the exercise period (e.g., HIIT:High-Intensity Interval Training, warm ups, or cool downs). Oneexplanation for determining the predicted final distance using a currentrank of a subject competitor is that it takes into consideration thatcompetitors are applying the same exercise structure and will tend tovary their speeds similarly. By contrast, a straight line projectioncould be misleading and unstable because the subject competitor willtend to drop in rank during “off intervals” and gain in rank during “onintervals.” Also, during a warm up period of an exercise activity, thesubject competitor's average speed will be slower than the average speedfor the full exercise activity. If a straight line projection techniquewere used in those circumstances, the projection could be misleadinglypoor during a warm up period.

Alternatives

Other implementations are within the scope of the following claims.

For example, although the earlier description has used rowing examples,the technology is also applicable to other kinds of exercise equipmentand exercise activities, such as cycling, walking, and running, or otheractivities in which distance is a performance metric.

In some implementations, the technology could be used for any sport oractivity involving “historical competitors” who are “in the clubhouse”competing with active competitors. The technology could be used fortelevised or broadcast activities as well as activities presented on theinternet (such as ESPN.net). The technology could also be used foronline gaming, such as racing games.

Communication architectures other than client-server could be applied insome implementations, including peer-to-peer architectures, for example.

The predefined scope could be a distance rather than a time, or could beone or more other parameters. The performance metric includes a timepredicted to have elapsed for the predefined scope of the exerciseactivity. The performance metric could be one or more other parameters.

In some cases, the rank listing can present a ranked list ofperformances according to the performance metrics without presenting anyidentifying information about the one or more of the virtual competitorsfor whom the ranked performance metrics are presented.

In some examples, the rank listing technology can be applied tocompetitions in which the subject competitor is competing against one ormore other live competitors who are performing the exercise activity inreal time with the subject competitor. In some instances, one or morevirtual competitors also can be included. We sometimes refer to suchcompetitions as occurring in “live mode.” In live mode, although thetechnology cannot predict the performance of another live competitorbased on her final data on the current exercise activity, the technologycan predict that performance based on her past performance of theexercise activity.

1. A computer-implemented method, comprising: in real time during acurrent instance of an exercise activity by a subject competitor,updating a graphical user interface displayed on a display device,wherein the display device is included in a first exercise machineoperated by the subject competitor or included in a mobile electronicdevice accessible to the subject competitor, wherein the graphical userinterface comprises: a ranking of the subject competitor and a secondcompetitor based on (i) a projected performance metric of the subjectcompetitor over a predefined scope of the exercise activity incomparison to (ii) a historical performance metric of the secondcompetitor over the predefined scope of the exercise activity in aprevious instance of the exercise activity, an illustration of a marginbetween the historical performance metric of the second competitor andthe projected performance metric for the subject competitor, and anillustration of the projected performance metric.
 2. Thecomputer-implemented method of claim 1, wherein the graphical userinterface comprises a grid-formatted display along a first dimension anda second dimension, wherein an order in which the subject competitor andthe second competitor are presented along the second dimension is basedon the ranking, wherein a linear display along the second dimension ofthe ranking includes, in a first line along the first dimensioncorresponding to the second competitor, the margin between thehistorical performance metric of the second competitor and the projectedperformance metric for the subject competitor, and wherein the lineardisplay along the second dimension includes, in a second line along thefirst dimension corresponding to the subject competitor, the projectedperformance metric.
 3. The computer-implemented method of claim 1,wherein the projected performance metric comprises a time or a distance.4. The computer-implemented method of claim 1, wherein the graphicaluser interface is updated at a time when the subject competitor hascompleted a lesser scope than the predefined scope of the exerciseactivity, and wherein the method comprises calculating the projectedperformance metric, wherein calculating the projected performance metriccomprises: identifying the second competitor as a competitor whosehistorical performance data over the lesser scope is next-best, amonghistorical performance data of a plurality of virtual competitors,compared to current performance data of the subject competitor over thelesser scope; and based on identifying the second competitor as thecompetitor whose historical performance data over the lesser scope isnext-best compared to the current performance data of the subjectcompetitor, calculating the projected performance metric based on thehistorical performance data of the second competitor over the lesserscope, the current performance data of the subject competitor over thelesser scope, and a proportion of the predefined scope represented bythe lesser scope.
 5. The computer-implemented method of claim 1,comprising: receiving, at the first exercise machine or at the mobileelectronic device, an update to the graphical user interface, whereinthe update is received from a remote server.
 6. The computer-implementedmethod of claim 1, wherein the graphical user interface comprises a userinterface element selectable to filter competitors from the ranking. 7.The computer-implemented method of claim 1, wherein the graphical userinterface comprises an option to display a current performance metric ofthe subject competitor.